We chose to use the RGB skeleton over a binary image of the pose. Our decision was based on the observation that color coded feature presentation are more effective. The other important observation we made while experimenting with Poly-GAN is that the results are affected by the order in which the conditions are concatenated and we adjusted the inputs accordingly. We used hyperparameters similar to CycleGAN [1]} for Poly-GAN training. We used a learning rate of 0.0002, an Adam optimizer with \(\beta _{1}=0.5\)  and \(\beta _{1}=0.999\) , batch size of 1 in all our experiments, and image size of 128x128 to feed images in the network. We also used an image buffer during training similar to the one suggested in [2]}. The image buffer helps in stabilizing the discriminator by keeping a history of a fixed number of generated images which are randomly passed to the discriminator. All the experiments are performed on a workstation with 16GB of RAM using an Nvidia 1080ti GPU and Ubuntu 16.04.
